Land use type interpretation and dynamic changes due to water storage projects using support vector machine
Received date: 2023-08-15
Revised date: 2024-01-19
Online published: 2024-04-26
In this study, to further restore and reconstruct the historical process of land use change before and after the construction of a water storage project and better grasp and forecast the direction of land use transfer, adaptive research on land use type interpretation was performed using the support vector machine theory. The adaptive adjustment ability and evolution direction of the land use structure before and after the construction of a water storage project were analyzed by examining the dynamic change in land use. The main conclusions were as follows: (1) The overall classification accuracy of the support vector machine for land use type interpretation is as high as 91.7%, and the Kappa coefficient is 0.90, depending on the advantages of self-learning and self-adaptation. In contrast with the relatively low accuracy observed for cultivated land producers, higher classification and recognition ability was observed other land types such as water bodies and forest land. (2) The Google Earth Engine (GEE) platform was used to examine the evolution process of land use types; it was found that the implementation of the second stage of the “Three-North Shelterbelt” project (2001-2020) significantly increased the area of construction land and forest land and increased the area of forest land by nearly five times compared with the initial stage of implementation in 2000. (3) Since the construction and operation of the project, nearly two-thirds of the area of forest land and construction land have maintained their original appearance, water bodies and unused land have been affected by water conservancy and urban construction projects, and more than 65% of the area has transformed from the original appearance type to other types. The “Three-North Shelterbelt” project accelerated the increase in forest area and improvement in grassland vegetation cover, and the net increase in the transformation of low-cover grassland to medium and high-cover grassland was 48.0% and 50.2%, respectively.
Key words： landuse; support vector machine; state transfer; water storage project
WANG Jun , CHAI Zhifu , MA Haoyan , ZHAO Zhimeng , WU Jiabin , FU Weiping . Land use type interpretation and dynamic changes due to water storage projects using support vector machine[J]. Arid Zone Research, 2024 , 41(4) : 581 -589 . DOI: 10.13866/j.azr.2024.04.05
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